Machine Learning Toolbox for Social Scientists: Applied Predictive Analytics with R

Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targeted at students and researchers who have no advanced statistical background, but instead coming from the tradition of "inferential statistics". The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields. Key Features: The book is structured for those who have been trained in a traditional statistics curriculum. There is one long initial section that covers the differences in "estimation" and "prediction" for people trained for causal analysis. The book develops a background framework for Machine learning applications from Nonparametric methods. SVM and NN simple enough without too much detail. It’s self-sufficient. Nonparametric time-series predictions are new and covered in a separate section. Additional sections are added: Penalized Regressions, Dimension Reduction Methods, and Graphical Methods have been increasing in their popularity in social sciences.

Table of Contents:
1. How We Define Machine Learning 2. Preliminaries Part 1. Formal Look at Prediction 3. Bias-Variance Tradeoff 4. Overfitting Part 2. Nonparametric Estimations 5. Parametric Estimations 6. Nonparametric Estimations - Basics 7. Smoothing 8. Nonparametric Classifier - kNN Part 3. Self-learning 9. Hyperparameter Tuning 10. Tuning in Classification 11. Classification Example Part 4. Tree-based Models 12. CART 13. Ensemble Learning 14. Ensemble Applications Part 5. SVM & Neural Networks 15. Support Vector Machines 16. Artificial Neural Networks Part 6. Penalized Regressions 17. Ridge 18. Lasso 19. Adaptive Lasso 20. Sparsity Part 7. Time Series Forecasting 21. ARIMA models 22. Grid Search for Arima 23. Time Series Embedding 24. Random Forest with Times Series 25. Recurrent Neural Networks Part 8. Dimension Reduction Methods 26. Eigenvectors and eigenvalues 27. Singular Value Decomposition 28. Rank r approximations 29. Moore-Penrose Inverse 30. Principle Component Analysis 31. Factor Analysis Part 9. Network Analysis 32. Fundamentals 33. Regularized Covariance Matrix Part 10. R Labs 34. R Lab 1 Basics 35. R Lab 2 Basics II 36. Simulations in R 37. Algorithmic Optimization 38. Imbalanced Data

About the Author :
Yigit Aydede is a Sobey Professor of Economics at Saint Mary’s University, Halifax, Nova Scotia, Canada. He is a founder member of the Research Portal on Machine Learning for Social and Health Policy, a joint initiative by a group of researchers from Saint Mary’s and Dalhousie universities